In many businesses, RPA developers and data scientists don't work together. However, their skills are extremely complementary.

To effectively introduce AI into the business and start your digital transformation, it is essential to bring together teams of RPA developers and data scientists.. Indeed, both teams want to contribute to the development of more efficient and smarter business processes and decisions, but that doesn't mean they're working together. There is usually an organizational divide between teams, causing them to use unnecessarily distinct means to reach similar destinations..

So what are the challenges associated with the work of RPA developers and data scientists and how can better governance be implemented to take advantage of these two essential parts of the business?

Why do businesses often misunderstand data scientists?

There are four main reasons why businesses underestimate the value of data scientists:

• Their commercial value is difficult to assess.. According to a survey byAnaconda Data Science, less than half (48%) of data scientists believe they can demonstrate the impact of data science on business outcomes.
• The return on investment is expensive. Data scientists - who are already expensive - often need more resources than businesses are prepared to invest. If they do not have these necessary resources, they are not ready to invest in their work.
• Their work brings no added value without collaboration. It is not always easy to put data science results into production, where they can have an impact on the business.
• A large part of their efforts is devoted to invisible work.. This can be immensely frustrating for data scientists.

When these four reasons combine, businesses tend to underestimate and underutilize their data scientists. They are often far from suspecting that a team is enough to unleash their value.

RPA developers also have trouble understanding data scientists

The mindset of developers RPA and data scientists tend to be different because they have different workflows and deadlines. Indeed, RPA developers, immersed in faster workflows, tend to think in terms of quick solutions, while data scientists tend to focus on more exploratory projects.. It also makes it difficult for these teams to communicate across departments, creating silos.

The skills of RPA developers and data scientists: different but complementary

When leaders combine RPA developers and data scientists, the benefits they can bring to organizations are greater than the sum of their parts. An RPA developer can automate much more complex processes by working with a data scientist than by working alone, and a data scientist working with an RPA developer can work more quickly.

Despite the divide we've described, RPA developers and data scientists speak the same language—or at least code it. In fact, the report State of RPA Developers Report 2020 from our partner UiPath shows that over 90% of RPA developers have a university degree and that Python is already one of the main languages known to RPA developers. The knowledge gap is not as wide as one might expect.

There is also a desire to bridge this gap.. Indeed, RPA developers often want to learn more about data science topics. Additionally, research shows that data scientists spend nearly half of their time on problems that RPA developers can solve more effectively and quickly.

A question therefore arises: how to bring together these clearly complementary teams?

Breaking the wall between data science and RPA teams

If leaders can break down the barriers between these teams, they can unlock massive opportunities for their businesses. To do this, leaders need to enable data scientists to communicate their needs to RPA developers and coordinate the two teams to achieve better results on complex problems.

First, the respective leaders can ensure that the two teams are talking to each other and that the communication is truly two-way.

Additionally, RPA developers can help data scientists. When data scientists run into a problem, RPA developers can come to the rescue.

For example, RPA developers can help data scientists by:

• The creation of metadata. Software robots, especially when complemented by process mining, leave traces of data as they complete tasks, making processes more understandable for data scientists.
• Access to existing systems. Software robots work with existing systems and make data that was previously trapped in old tools accessible.
• Access to more usable data. RPA developers can organize large, often disparate, data sets into a coherent whole.
• The implementation of ready-to-use AI modules. A lot of machine and AI use cases aren't new. Instead of data scientists building models from scratch, RPA developers can deploy pre-existing modules.

Where is the result?

These combined benefits not only make life easier for data scientists, but also help them achieve more than they could before. So, RPA developers allow data scientists to do their jobs faster, while making the final solution easier to deploy.

About StoryShaper:

StoryShaper is an innovative start-up that supports its customers in defining their digital strategy and the development of automation solutions tailor-made.

Sources: StoryShaper, UiPath